OpenRaiser/Intern-Atlas huggingface dataset:
Secure Two-Way Transmission via Wireless-Powered Untrusted Relay and External Jammer
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we propose a two-way secure communication scheme where two transceivers exchange confidential messages via a wireless powered untrusted amplify-and-forward (AF) relay in the presence of an external jammer. We take into account both friendly jamming (FJ) and Gaussian noise jamming (GNJ) scenarios. Based on the time switching (TS) architecture at the relay, the data transmission is done in three phases. In the first phase, both the energy-starved nodes, the untrustworthy relay and the jammer, are charged by non-information radio frequency (RF) signals from the sources. In the second phase, the two sources send their information signals and concurrently, the jammer transmits artificial noise to confuse the curious relay. Finally, the third phase is dedicated to forward a scaled version of the received signal from the relay to the sources. For the proposed secure transmission schemes, we derive new closed-form lower-bound expressions for the ergodic secrecy sum rate (ESSR) in the high signal-to-noise ratio (SNR) regime. We further analyze the asymptotic ESSR to determine the key parameters; the high SNR slope and the high SNR power offset of the jamming based scenarios. To highlight the performance advantage of the proposed FJ, we also examine the scenario of without jamming (WoJ). Finally, numerical examples and discussions are provided to acquire some engineering insights, and to demonstrate the impacts of different system parameters on the secrecy performance of the considered communication scenarios. The numerical results illustrate that the proposed FJ significantly outperforms the traditional one-way communication and the Constellation rotation approach, as well as our proposed benchmarks, the two-way WoJ and GNJ scenarios.
Contrast and visual saliency similarity-induced index for assessing image quality
Venue: arXiv cs.CV 2018
Year: 2018

Image quality that is consistent with human opinion is assessed by a perceptual image quality assessment (IQA) that defines/utilizes a computational model. A good model should take effectiveness and efficiency into consideration, but most of the previously proposed IQA models do not simultaneously consider these factors. Therefore, this study attempts to develop an effective and efficient IQA metric. Contrast is an inherent visual attribute that indicates image quality, and visual saliency (VS) is a quality that attracts the attention of human beings. The proposed model utilized these two features to characterize the image local quality. After obtaining the local contrast quality map and the global VS quality map, we added the weighted standard deviation of the previous two quality maps together to yield the final quality score. The experimental results for three benchmark databases (LIVE, TID2008, and CSIQ) demonstrated that our model performs the best in terms of a correlation with the human judgment of visual quality. Furthermore, compared with competing IQA models, this proposed model is more efficient.
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Venue: arXiv cs.CL 2018
Year: 2018

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.
Community Targeted Phishing: A Middle Ground Between Massive and Spear Phishing through Natural Language Generation
Venue: arXiv cs.CR 2018
Year: 2018

Looking at today phishing panorama, we are able to identify two diametrically opposed approaches. On the one hand, massive phishing targets as many people as possible with generic and preformed texts. On the other hand, spear phishing targets high-value victims with hand-crafted emails. While nowadays these two worlds partially intersect, we envision a future where Natural Language Generation (NLG) techniques will enable attackers to target populous communities with machine-tailored emails. In this paper, we introduce what we call Community Targeted Phishing (CTP), alongside with some workflows that exhibit how NLG techniques can craft such emails. Furthermore, we show how Advanced NLG techniques could provide phishers new powerful tools to bring up to the surface new information from complex data-sets, and use such information to threaten victims' private data.
Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

When there are a large number of antennas in massive MIMO systems, the transmitted wideband signal will be sensitive to the physical propagation delay of electromagnetic waves across the large array aperture, which is called the spatial-wideband effect. In this scenario, transceiver design is different from most of the existing works, which presume that the bandwidth of the transmitted signals is not that wide, ignore the spatial-wideband effect, and only address the frequency selectivity. In this paper, we investigate spatial- and frequency-wideband effects, called dual-wideband effects, in massive MIMO systems from array signal processing point of view. Taking mmWave-band communications as an example, we describe the transmission process to address the dual-wideband effects. By exploiting the channel sparsity in the angle domain and the delay domain, we develop the efficient uplink and downlink channel estimation strategies that require much less amount of training overhead and cause no pilot contamination. Thanks to the array signal processing techniques, the proposed channel estimation is suitable for both TDD and FDD massive MIMO systems. Numerical examples demonstrate that the proposed transmission design for massive MIMO systems can effectively deal with the dual-wideband effects.
A dependency look at the reality of constituency
Venue: arXiv cs.CL 2018
Year: 2018

A comment on "Neurophysiological dynamics of phrase-structure building during sentence processing" by Nelson et al (2017), Proceedings of the National Academy of Sciences USA 114(18), E3669-E3678.
Robust Stereo Feature Descriptor for Visual Odometry
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a simple way to utilize stereo camera data to improve feature descriptors. Computer vision algorithms that use a stereo camera require some calculations of 3D information. We leverage this pre-calculated information to improve feature descriptor algorithms. We use the 3D feature information to estimate the scale of each feature. This way, each feature descriptor will be more robust to scale change without significant computations. In addition, we use stereo images to construct the descriptor vector. The Scale-Invariant Feature Transform (SIFT) and Fast Retina Keypoint (FREAK) descriptors are used to evaluate the proposed method. The scale normalization technique in feature tracking test improves the standard SIFT by 8.75% and improves the standard FREAK by 28.65%. Using the proposed stereo feature descriptor, a visual odometry algorithm is designed and tested on the KITTI dataset. The stereo FREAK descriptor raises the number of inlier matches by 19% and consequently improves the accuracy of visual odometry by 23%.
Generalized Short Circuit Ratio for Multi Power Electronic based Devices Infeed Systems: Defi-nition and Theoretical Analysis
Venue: arXiv cs.SY 2018
Year: 2018

Short circuit ratio (SCR) is widely applied to analyze the strength of AC system and the small signal stability for single power elec-tronic based devices infeed systems (SPEISs). However, there still lacking the theory of short circuit ratio applicable for multi power electronic based devices infeed systems (MPEIS), as the complex coupling among multi power electronic devices (PEDs) leads to difficulties in stability analysis. In this regard, this paper firstly proposes a concept named generalized short circuit ratio (gSCR) to measure the strength of connected AC grid in a multi-infeed system from the small signal stability point of view. Generally, the gSCR is physically and mathematically extended from conven-tional SCR by decomposing the multi-infeed system into n inde-pendent single infeed systems. Then the operation gSCR (OgSCR) is proposed based on gSCR in order to take the variation of op-eration point into consideration. The participation factors and sensitivity are analyzed as well. Finally, simulations are conducted to demonstrate the rationality and effectiveness of the defined gSCR and OgSCR.
RIOT: a Stochastic-based Method for Workflow Scheduling in the Cloud
Venue: arXiv cs.SE 2018
Year: 2018

Cloud computing provides engineers or scientists a place to run complex computing tasks. Finding a workflow's deployment configuration in a cloud environment is not easy. Traditional workflow scheduling algorithms were based on some heuristics, e.g. reliability greedy, cost greedy, cost-time balancing, etc., or more recently, the meta-heuristic methods, such as genetic algorithms. These methods are very slow and not suitable for rescheduling in the dynamic cloud environment. This paper introduces RIOT (Randomized Instance Order Types), a stochastic based method for workflow scheduling. RIOT groups the tasks in the workflow into virtual machines via a probability model and then uses an effective surrogate-based method to assess a large amount of potential scheduling. Experiments in dozens of study cases showed that RIOT executes tens of times faster than traditional methods while generating comparable results to other methods.
Scheduling Constraint Based Abstraction Refinement for Multi-Threaded Program Verification
Venue: arXiv cs.PL 2018
Year: 2018

Bounded model checking is among the most efficient techniques for the automatic verification of concurrent programs. However, encoding all possible interleavings often requires a huge and complex formula, which significantly limits the salability. This paper proposes a novel and efficient abstraction refinement method for multi-threaded program verification. Observing that the huge formula is usually dominated by the exact encoding of the scheduling constraint, this paper proposes a \tsc based abstraction refinement method, which avoids the huge and complex encoding of BMC. In addition, to obtain an effective refinement, we have devised two graph-based algorithms over event order graph for counterexample validation and refinement generation, which can always obtain a small yet effective refinement constraint. Enhanced by two constraint-based algorithms for counterexample validation and refinement generation, we have proved that our method is sound and complete w.r.t. the given loop unwinding depth. Experimental results on \svcompc benchmarks indicate that our method is promising and significantly outperforms the existing state-of-the-art tools.
A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem
Venue: arXiv cs.SY 2018
Year: 2018

Disturbance noises are always bounded in a practical system, while fusion estimation is to best utilize multiple sensor data containing noises for the purpose of estimating a quantity--a parameter or process. However, few results are focused on the information fusion estimation problem under bounded noises. In this paper, we study the distributed fusion estimation problem for linear time-varying systems and nonlinear systems with bounded noises, where the addressed noises do not provide any statistical information, and are unknown but bounded. When considering linear time-varying fusion systems with bounded noises, a new local Kalman-like estimator is designed such that the square error of the estimator is bounded as time goes to $\infty$. A novel constructive method is proposed to find an upper bound of fusion estimation error, then a convex optimization problem on the design of an optimal weighting fusion criterion is established in terms of linear matrix inequalities, which can be solved by standard software packages. Furthermore, according to the design method of linear time-varying fusion systems, each local nonlinear estimator is derived for nonlinear systems with bounded noises by using Taylor series expansion, and a corresponding distributed fusion criterion is obtained by solving a convex optimization problem. Finally, target tracking system and localization of a mobile robot are given to show the advantages and effectiveness of the proposed methods.
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
Venue: arXiv cs.CV 2018
Year: 2018

The recently proposed Multi-Layer Convolutional Sparse Coding (ML-CSC) model, consisting of a cascade of convolutional sparse layers, provides a new interpretation of Convolutional Neural Networks (CNNs). Under this framework, the computation of the forward pass in a CNN is equivalent to a pursuit algorithm aiming to estimate the nested sparse representation vectors -- or feature maps -- from a given input signal. Despite having served as a pivotal connection between CNNs and sparse modeling, a deeper understanding of the ML-CSC is still lacking: there are no pursuit algorithms that can serve this model exactly, nor are there conditions to guarantee a non-empty model. While one can easily obtain signals that approximately satisfy the ML-CSC constraints, it remains unclear how to simply sample from the model and, more importantly, how one can train the convolutional filters from real data. In this work, we propose a sound pursuit algorithm for the ML-CSC model by adopting a projection approach. We provide new and improved bounds on the stability of the solution of such pursuit and we analyze different practical alternatives to implement this in practice. We show that the training of the filters is essential to allow for non-trivial signals in the model, and we derive an online algorithm to learn the dictionaries from real data, effectively resulting in cascaded sparse convolutional layers. Last, but not least, we demonstrate the applicability of the ML-CSC model for several applications in an unsupervised setting, providing competitive results. Our work represents a bridge between matrix factorization, sparse dictionary learning and sparse auto-encoders, and we analyze these connections in detail.
A Scalable Multiphysics Algorithm for Massively Parallel Direct Numerical Simulations of Electrophoresis
Venue: arXiv cs.CE 2018
Year: 2018

In this article we introduce a novel coupled algorithm for massively parallel direct numerical simulations of electrophoresis in microfluidic flows. This multiphysics algorithm employs an Eulerian description of fluid and ions, combined with a Lagrangian representation of moving charged particles. The fixed grid facilitates efficient solvers and the employed lattice Boltzmann method can efficiently handle complex geometries. Validation experiments with more than $70\,000$ time steps are presented, together with scaling experiments with over ${4\cdot10^{6}}$ particles and ${1.96\cdot10^{11}}$ grid cells for both hydrodynamics and electric potential. We achieve excellent performance and scaling on up to $65\,536$ cores of a current supercomputer.
Navigating the Data Lake with Datamaran: Automatically Extracting Structure from Log Datasets
Venue: arXiv cs.DB 2018
Year: 2018

Organizations routinely accumulate semi-structured log datasets generated as the output of code; these datasets remain unused and uninterpreted, and occupy wasted space - this phenomenon has been colloquially referred to as "data lake" problem. One approach to leverage these semi-structured datasets is to convert them into a structured relational format, following which they can be analyzed in conjunction with other datasets. We present Datamaran, an tool that extracts structure from semi-structured log datasets with no human supervision. Datamaran automatically identifies field and record endpoints, separates the structured parts from the unstructured noise or formatting, and can tease apart multiple structures from within a dataset, in order to efficiently extract structured relational datasets from semi-structured log datasets, at scale with high accuracy. Compared to other unsupervised log dataset extraction tools developed in prior work, Datamaran does not require the record boundaries to be known beforehand, making it much more applicable to the noisy log files that are ubiquitous in data lakes. Datamaran can successfully extract structured information from all datasets used in prior work, and can achieve 95% extraction accuracy on automatically collected log datasets from GitHub - a substantial 66% increase of accuracy compared to unsupervised schemes from prior work. Our user study further demonstrates that the extraction results of Datamaran are closer to the desired structure than competing algorithms.
Identity Testing and Interpolation from High Powers of Polynomials of Large Degree over Finite Fields
Venue: arXiv cs.CC 2018
Year: 2018

We consider the problem of identity testing and recovering (that is, interpolating) of a "hidden" monic polynomials $f$, given an oracle access to $f(x)^e$ for $x\in\mathbb F_q$, where $\mathbb F_q$ is the finite field of $q$ elements and an extension fields access is not permitted. The naive interpolation algorithm needs $de+1$ queries, where $d =\max\{{\rm deg}\ f, {\rm deg }\ g\}$ and thus requires $ de<q$. For a prime $q = p$, we design an algorithm that is asymptotically better in certain cases, especially when $d$ is large. The algorithm is based on a result of independent interest in spirit of additive combinatorics. It gives an upper bound on the number of values of a rational function of large degree, evaluated on a short sequence of consecutive integers, that belong to a small subgroup of $\mathbb F_p^*$.
Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography
Venue: arXiv cs.CV 2018
Year: 2018

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an "end-to-end" training approach that efficiently leverages training datasets with either complete clinical annotation or only the cancer status (label) of the whole image. In this approach, lesion annotations are required only in the initial training stage, and subsequent stages require only image-level labels, eliminating the reliance on rarely available lesion annotations. Our all convolutional network method for classifying screening mammograms attained excellent performance in comparison with previous methods. On an independent test set of digitized film mammograms from Digital Database for Screening Mammography (DDSM), the best single model achieved a per-image AUC of 0.88, and four-model averaging improved the AUC to 0.91 (sensitivity: 86.1%, specificity: 80.1%). On a validation set of full-field digital mammography (FFDM) images from the INbreast database, the best single model achieved a per-image AUC of 0.95, and four-model averaging improved the AUC to 0.98 (sensitivity: 86.7%, specificity: 96.1%). We also demonstrate that a whole image classifier trained using our end-to-end approach on the DDSM digitized film mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for fine-tuning and without further reliance on the availability of lesion annotations. These findings show that automatic deep learning methods can be readily trained to attain high accuracy on heterogeneous mammography platforms, and hold tremendous promise for improving clinical tools to reduce false positive and false negative screening mammography results.
Video Summarization with Attention-Based Encoder-Decoder Networks
Venue: arXiv cs.CV 2018
Year: 2018

This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named Attentive encoder-decoder networks for Video Summarization (AVS), in which the encoder uses a Bidirectional Long Short-Term Memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on three video summarization benchmark datasets, i.e., SumMe, and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches,with remarkable improvements from 0.8% to 3% on two datasets,respectively..
Faster Multiplication for Long Binary Polynomials
Venue: arXiv cs.SC 2018
Year: 2018

We set new speed records for multiplying long polynomials over finite fields of characteristic two. Our multiplication algorithm is based on an additive FFT (Fast Fourier Transform) by Lin, Chung, and Huang in 2014 comparing to previously best results based on multiplicative FFTs. Both methods have similar complexity for arithmetic operations on underlying finite field; however, our implementation shows that the additive FFT has less overhead. For further optimization, we employ a tower field construction because the multipliers in the additive FFT naturally fall into small subfields, which leads to speed-ups using table-lookup instructions in modern CPUs. Benchmarks show that our method saves about $40 \%$ computing time when multiplying polynomials of $2^{28}$ and $2^{29}$ bits comparing to previous multiplicative FFT implementations.
Technology in Hospitality Industry: Prospects and Challenges
Venue: arXiv cs.DC 2018
Year: 2018

The leisure and hospitality industry is one of the driving forces of the global economy. The widespread adoption of new technologies in this industry over recent years has fundamentally reshaped the way in which services are provided and received. In this paper, we explore some of the state-of-the-art technologies currently employed in the hospitality industry and how they are improving guest experiences and changing the hospitality service platform. We also envision some potential future hospitality services we can expect as the Internet of things (IoT) technology keeps growing. We recognize that the technological backbone of many hospitality establishments needs to be overhauled in order to facilitate the changing landscape of technology in the modern world. We discuss some fundamental challenges that need to be overcome to institute a lasting future-proof solution for the hospitality industry. We also touch upon the problems these challenges pose for guests and hospitality service providers (HSP).